DOAJ Open Access 2025

Precision Agriculture: Tomato Disease Classification via Compact Convolutional Vision Transformer

Amine MEZENNER Mohamed Rayane LAKEHAL Naouel ARAB Hassiba NEMMOUR Youcef CHIBANI

Abstrak

Plant disease detection is a one of the most studied subjects in precision agriculture which aims to protect and improve agricultural crops. Commonly, intelligent systems based on CNN (Convolutional Neural Networks) are employed to identify multiple plant diseases by analyzing leaf images. In this work, we propose the use of the Compact Convolutional vision Transformer for tomato disease classification. Experiments conducted on a set of 10 tomato disease categories highlight the effectiveness of the proposed system which outperforms famous CNN models including DenseNet201, and MobileNetV2 by 1.73% in the overall classification accuracy.

Topik & Kata Kunci

Applications of electric power Electric apparatus and materials. Electric circuits. Electric networks

Penulis (5)

Amine MEZENNER

Mohamed Rayane LAKEHAL

Naouel ARAB

Hassiba NEMMOUR

Youcef CHIBANI

Format Sitasi

APA MLA BibTeX

MEZENNER, A., LAKEHAL, M.R., ARAB, N., NEMMOUR, H., CHIBANI, Y. (2025). Precision Agriculture: Tomato Disease Classification via Compact Convolutional Vision Transformer . https://doi.org/10.51485/ajss.v10i2.266

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Informasi Jurnal

Tahun Terbit: 2025
Sumber Database: DOAJ
DOI: 10.51485/ajss.v10i2.266
Akses: Open Access ✓